In [1]:
%%capture
!pip install kagglehub
Download the file from kagglehub. it's about 2.78 GB¶
In [2]:
!pip install kagglehub
import kagglehub
# Download latest version
path = kagglehub.dataset_download("imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music")
print("Path to dataset files:", path)
Requirement already satisfied: kagglehub in /usr/local/lib/python3.11/dist-packages (0.3.12) Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from kagglehub) (24.2) Requirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from kagglehub) (6.0.2) Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from kagglehub) (2.32.3) Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from kagglehub) (4.67.1) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->kagglehub) (3.4.2) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->kagglehub) (3.10) Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->kagglehub) (2.4.0) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->kagglehub) (2025.4.26) Downloading from https://www.kaggle.com/api/v1/datasets/download/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music?dataset_version_number=1...
100%|██████████| 1.83G/1.83G [00:12<00:00, 159MB/s]
Extracting files...
Path to dataset files: /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1
In [3]:
#just shows size of data
import os
def get_folder_size(start_path):
total_size = 0
for dirpath, dirnames, filenames in os.walk(start_path):
for f in filenames:
fp = os.path.join(dirpath, f)
# Skip if it’s a broken symlink
if os.path.isfile(fp):
total_size += os.path.getsize(fp)
return total_size
dataset_path = path
size_bytes = get_folder_size(dataset_path)
size_gb = size_bytes / (1024**3)
print(f" Total dataset size: {size_gb:.2f} GB")
Total dataset size: 2.78 GB
In [4]:
#lists all files downloaded
dataset_path = path
# List contents
for root, dirs, files in os.walk(dataset_path):
for file in files:
print(os.path.join(root, file))
/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1910.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1834.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1524.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1220.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1231.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1350.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1816.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/530.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/83.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1212.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/962.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/844.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1124.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1924.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/359.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/764.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/177.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1664.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/617.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/865.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/749.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/629.csv /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic 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In [5]:
for root, dirs, files in os.walk(dataset_path):
for file in files:
filepath = os.path.join(root, file)
size_mb = os.path.getsize(filepath) / (1024 * 1024)
print(f"{file:50} {size_mb:>8.2f} MB")
Streaming output truncated to the last 5000 lines.
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1399.csv 0.30 MB
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180.csv 2.22 MB
192.csv 0.39 MB
96.csv 1.07 MB
1996.csv 0.30 MB
51.csv 1.36 MB
379.csv 1.37 MB
524.csv 1.71 MB
1344.csv 0.29 MB
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81.csv 1.28 MB
556.csv 1.00 MB
1235.csv 0.30 MB
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1340.csv 0.30 MB
122.csv 1.06 MB
1990.csv 0.30 MB
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1136.csv 0.30 MB
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1487.csv 0.30 MB
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1745.csv 0.30 MB
709.csv 1.53 MB
728.csv 2.84 MB
554.csv 1.04 MB
1377.csv 0.30 MB
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44.csv 1.66 MB
1396.csv 0.30 MB
725.csv 0.89 MB
163.csv 2.77 MB
1673.csv 0.30 MB
1969.csv 0.30 MB
1791.csv 0.30 MB
955.csv 1.37 MB
1001.csv 0.30 MB
399.csv 1.44 MB
1691.csv 0.30 MB
869.csv 1.60 MB
718.csv 1.83 MB
1721.csv 0.30 MB
1688.csv 0.30 MB
397.csv 1.82 MB
349.csv 1.09 MB
1455.csv 0.30 MB
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1478.csv 0.30 MB
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1779.csv 0.30 MB
1593.csv 0.30 MB
2002.csv 0.85 MB
585.csv 0.58 MB
1753.csv 0.30 MB
621.csv 2.79 MB
1291.csv 0.30 MB
1869.csv 0.30 MB
1121.csv 0.30 MB
1678.csv 0.30 MB
1411.csv 0.30 MB
1215.csv 0.30 MB
646.csv 1.48 MB
614.csv 1.50 MB
129.csv 0.91 MB
695.csv 2.94 MB
1866.csv 0.30 MB
691.csv 2.46 MB
1250.csv 0.30 MB
1477.csv 0.30 MB
1727.csv 0.30 MB
71.csv 1.14 MB
1343.csv 0.30 MB
427.csv 1.11 MB
1734.csv 0.30 MB
892.csv 1.71 MB
1368.csv 0.30 MB
1728.csv 0.30 MB
1471.csv 0.30 MB
1040.csv 0.30 MB
903.csv 0.72 MB
448.csv 1.08 MB
1415.csv 0.29 MB
130.csv 2.02 MB
1702.csv 0.30 MB
1475.csv 0.30 MB
1087.csv 0.30 MB
1149.csv 0.30 MB
1998.csv 0.30 MB
650.csv 2.87 MB
1831.csv 0.30 MB
780.csv 0.77 MB
354.csv 1.21 MB
207.csv 0.51 MB
1442.csv 0.30 MB
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2048.mp3 2.32 MB
1710.mp3 0.69 MB
1105.mp3 0.69 MB
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253.mp3 0.60 MB
973.mp3 0.57 MB
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1158.mp3 0.69 MB
2057.mp3 2.79 MB
262.mp3 0.57 MB
1565.mp3 0.69 MB
280.mp3 0.53 MB
1519.mp3 0.69 MB
406.mp3 0.62 MB
307.mp3 0.57 MB
1456.mp3 0.69 MB
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1243.mp3 0.69 MB
1067.mp3 0.69 MB
836.mp3 0.59 MB
1414.mp3 0.69 MB
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1632.mp3 0.69 MB
582.mp3 0.57 MB
649.mp3 0.60 MB
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1658.mp3 0.69 MB
551.mp3 0.56 MB
964.mp3 0.63 MB
903.mp3 0.58 MB
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1640.mp3 0.35 MB
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190.mp3 0.53 MB
1727.mp3 0.69 MB
1281.mp3 0.69 MB
1268.mp3 0.69 MB
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1939.mp3 0.69 MB
1052.mp3 0.69 MB
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1432.mp3 0.69 MB
1709.mp3 0.69 MB
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1314.mp3 0.69 MB
1173.mp3 0.69 MB
1656.mp3 0.69 MB
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345.mp3 0.57 MB
296.mp3 0.60 MB
1351.mp3 0.69 MB
1690.mp3 0.69 MB
156.mp3 0.59 MB
353.mp3 0.45 MB
221.mp3 0.54 MB
1270.mp3 0.69 MB
756.mp3 0.60 MB
1162.mp3 0.69 MB
317.mp3 0.60 MB
614.mp3 0.56 MB
497.mp3 0.61 MB
629.mp3 0.61 MB
2000.mp3 0.69 MB
1393.mp3 0.69 MB
107.mp3 0.63 MB
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1176.mp3 0.69 MB
1940.mp3 0.69 MB
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1604.mp3 0.69 MB
1763.mp3 0.69 MB
1721.mp3 0.69 MB
1009.mp3 0.69 MB
925.mp3 0.54 MB
699.mp3 0.57 MB
1952.mp3 0.35 MB
334.mp3 0.56 MB
648.mp3 0.58 MB
214.mp3 0.55 MB
53.mp3 0.53 MB
1913.mp3 0.69 MB
365.mp3 0.58 MB
839.mp3 0.50 MB
1668.mp3 0.69 MB
371.mp3 0.58 MB
1593.mp3 0.69 MB
1246.mp3 0.69 MB
2010.mp3 3.09 MB
814.mp3 0.70 MB
1115.mp3 0.69 MB
1836.mp3 0.69 MB
681.mp3 0.59 MB
461.mp3 0.62 MB
1304.mp3 0.69 MB
1339.mp3 0.69 MB
1188.mp3 0.69 MB
1357.mp3 0.69 MB
489.mp3 0.59 MB
876.mp3 0.60 MB
395.mp3 0.59 MB
243.mp3 0.53 MB
43.mp3 0.65 MB
1936.mp3 0.69 MB
164.mp3 0.54 MB
1663.mp3 0.69 MB
2013.mp3 4.04 MB
1612.mp3 0.69 MB
1789.mp3 0.69 MB
1581.mp3 0.69 MB
1291.mp3 0.69 MB
487.mp3 0.60 MB
1829.mp3 0.69 MB
1869.mp3 0.35 MB
1945.mp3 0.69 MB
2008.mp3 3.29 MB
1403.mp3 0.69 MB
1859.mp3 0.69 MB
1505.mp3 0.69 MB
728.mp3 0.59 MB
732.mp3 0.57 MB
180.mp3 0.54 MB
1521.mp3 0.69 MB
1334.mp3 0.69 MB
2047.mp3 4.43 MB
1228.mp3 0.69 MB
1780.mp3 0.69 MB
1963.mp3 0.69 MB
211.mp3 0.54 MB
1697.mp3 0.69 MB
1460.mp3 0.69 MB
1748.mp3 0.69 MB
1265.mp3 0.69 MB
1779.mp3 0.69 MB
1006.mp3 0.69 MB
1705.mp3 0.69 MB
1007.mp3 0.69 MB
370.mp3 0.51 MB
1073.mp3 0.69 MB
686.mp3 0.55 MB
1068.mp3 0.69 MB
1646.mp3 0.69 MB
1644.mp3 0.69 MB
979.mp3 0.56 MB
1341.mp3 0.69 MB
1734.mp3 0.69 MB
2011.mp3 9.57 MB
451.mp3 0.57 MB
1844.mp3 0.69 MB
1161.mp3 0.69 MB
120.mp3 0.56 MB
175.mp3 0.55 MB
1172.mp3 0.69 MB
1276.mp3 0.69 MB
178.mp3 0.73 MB
137.mp3 0.41 MB
1032.mp3 0.69 MB
319.mp3 0.52 MB
1607.mp3 0.69 MB
204.mp3 0.53 MB
1953.mp3 0.69 MB
343.mp3 0.58 MB
1233.mp3 0.69 MB
1418.mp3 0.69 MB
181.mp3 0.54 MB
276.mp3 0.58 MB
775.mp3 0.57 MB
1718.mp3 0.69 MB
540.mp3 0.58 MB
826.mp3 0.55 MB
1541.mp3 0.69 MB
1769.mp3 0.69 MB
880.mp3 0.64 MB
959.mp3 0.61 MB
1947.mp3 0.69 MB
1572.mp3 0.69 MB
1664.mp3 0.69 MB
1363.mp3 0.69 MB
1049.mp3 0.69 MB
119.mp3 0.57 MB
1085.mp3 0.69 MB
1122.mp3 0.69 MB
1961.mp3 0.69 MB
467.mp3 0.63 MB
1903.mp3 0.69 MB
989.mp3 0.65 MB
1043.mp3 0.69 MB
1954.mp3 0.69 MB
717.mp3 0.57 MB
1848.mp3 0.69 MB
452.mp3 0.58 MB
1148.mp3 0.69 MB
207.mp3 0.58 MB
735.mp3 0.59 MB
1980.mp3 0.69 MB
1786.mp3 0.69 MB
285.mp3 0.57 MB
1549.mp3 0.69 MB
1462.mp3 0.69 MB
1551.mp3 0.69 MB
1146.mp3 0.69 MB
1666.mp3 0.69 MB
1931.mp3 0.69 MB
154.mp3 0.56 MB
924.mp3 0.62 MB
1927.mp3 0.69 MB
1079.mp3 0.69 MB
1204.mp3 0.69 MB
1842.mp3 0.69 MB
1368.mp3 0.69 MB
1299.mp3 0.69 MB
1538.mp3 0.69 MB
473.mp3 0.66 MB
1500.mp3 0.69 MB
1576.mp3 0.69 MB
729.mp3 0.58 MB
519.mp3 0.60 MB
1716.mp3 0.69 MB
606.mp3 0.56 MB
1101.mp3 0.69 MB
1096.mp3 0.69 MB
1573.mp3 0.69 MB
1514.mp3 0.69 MB
1578.mp3 0.69 MB
1305.mp3 0.69 MB
1728.mp3 0.69 MB
1511.mp3 0.69 MB
7.mp3 0.59 MB
908.mp3 0.60 MB
187.mp3 0.46 MB
1292.mp3 0.69 MB
1630.mp3 0.69 MB
293.mp3 0.60 MB
1636.mp3 0.69 MB
718.mp3 0.59 MB
1732.mp3 0.69 MB
926.mp3 0.51 MB
848.mp3 0.58 MB
521.mp3 0.56 MB
1303.mp3 0.69 MB
111.mp3 0.56 MB
830.mp3 0.65 MB
269.mp3 0.59 MB
713.mp3 0.60 MB
1974.mp3 0.69 MB
1406.mp3 0.69 MB
850.mp3 0.57 MB
820.mp3 0.60 MB
152.mp3 0.58 MB
795.mp3 0.53 MB
986.mp3 0.57 MB
1547.mp3 0.69 MB
1163.mp3 0.69 MB
1713.mp3 0.69 MB
957.mp3 0.56 MB
1807.mp3 0.69 MB
1220.mp3 0.69 MB
1426.mp3 0.69 MB
1266.mp3 0.69 MB
983.mp3 0.61 MB
301.mp3 0.57 MB
1419.mp3 0.69 MB
1905.mp3 0.69 MB
1331.mp3 0.69 MB
1342.mp3 0.69 MB
450.mp3 0.55 MB
1097.mp3 0.69 MB
375.mp3 0.58 MB
845.mp3 0.46 MB
2049.mp3 4.34 MB
1925.mp3 0.69 MB
1781.mp3 0.69 MB
856.mp3 0.59 MB
1599.mp3 0.69 MB
2035.mp3 1.62 MB
206.mp3 0.48 MB
1556.mp3 0.69 MB
955.mp3 0.57 MB
1118.mp3 0.69 MB
742.mp3 0.59 MB
1090.mp3 0.69 MB
1555.mp3 0.69 MB
1130.mp3 0.69 MB
52.mp3 0.63 MB
1365.mp3 0.69 MB
1577.mp3 0.69 MB
1360.mp3 0.69 MB
1930.mp3 0.69 MB
1477.mp3 0.69 MB
1000.mp3 0.59 MB
1058.mp3 0.69 MB
112.mp3 0.58 MB
738.mp3 0.59 MB
1649.mp3 0.69 MB
1140.mp3 0.69 MB
1241.mp3 0.69 MB
1248.mp3 0.35 MB
1667.mp3 0.35 MB
359.mp3 0.57 MB
432.mp3 0.64 MB
1766.mp3 0.69 MB
1642.mp3 0.69 MB
2058.mp3 4.19 MB
464.mp3 0.63 MB
913.mp3 0.56 MB
1988.mp3 0.69 MB
355.mp3 0.57 MB
1948.mp3 0.69 MB
77.mp3 0.61 MB
208.mp3 0.58 MB
1864.mp3 0.69 MB
69.mp3 0.51 MB
1976.mp3 0.69 MB
865.mp3 0.54 MB
1408.mp3 0.69 MB
1041.mp3 0.69 MB
492.mp3 0.56 MB
347.mp3 0.45 MB
1794.mp3 0.69 MB
326.mp3 0.61 MB
195.mp3 0.55 MB
210.mp3 0.54 MB
51.mp3 0.59 MB
1608.mp3 0.69 MB
1774.mp3 0.69 MB
1960.mp3 0.69 MB
2002.mp3 1.99 MB
1602.mp3 0.69 MB
2019.mp3 2.75 MB
1431.mp3 0.69 MB
1147.mp3 0.69 MB
1219.mp3 0.69 MB
423.mp3 0.64 MB
1762.mp3 0.69 MB
1520.mp3 0.69 MB
1821.mp3 0.69 MB
1731.mp3 0.69 MB
654.mp3 0.58 MB
1834.mp3 0.69 MB
126.mp3 0.60 MB
1837.mp3 0.69 MB
640.mp3 0.60 MB
209.mp3 0.53 MB
1817.mp3 0.69 MB
645.mp3 0.59 MB
1737.mp3 0.69 MB
2022.mp3 3.92 MB
1742.mp3 0.69 MB
1080.mp3 0.69 MB
479.mp3 0.58 MB
954.mp3 0.60 MB
2.mp3 0.57 MB
1093.mp3 0.69 MB
1554.mp3 0.69 MB
374.mp3 0.58 MB
237.mp3 0.58 MB
1287.mp3 0.69 MB
1018.mp3 0.69 MB
620.mp3 0.59 MB
169.mp3 0.54 MB
2052.mp3 2.96 MB
1635.mp3 0.69 MB
161.mp3 0.50 MB
1151.mp3 0.69 MB
1674.mp3 0.69 MB
1601.mp3 0.69 MB
1409.mp3 0.69 MB
149.mp3 0.50 MB
1661.mp3 0.69 MB
403.mp3 0.56 MB
1468.mp3 0.69 MB
329.mp3 0.57 MB
591.mp3 0.57 MB
1826.mp3 0.69 MB
625.mp3 0.54 MB
941.mp3 0.62 MB
806.mp3 0.60 MB
35.mp3 0.62 MB
692.mp3 0.61 MB
1726.mp3 0.69 MB
1784.mp3 0.69 MB
703.mp3 0.59 MB
1353.mp3 0.69 MB
1221.mp3 0.69 MB
1628.mp3 0.69 MB
1679.mp3 0.69 MB
1882.mp3 0.69 MB
1072.mp3 0.69 MB
1670.mp3 0.69 MB
1141.mp3 0.69 MB
122.mp3 0.63 MB
899.mp3 0.50 MB
1672.mp3 0.69 MB
1957.mp3 0.69 MB
2028.mp3 4.63 MB
274.mp3 0.61 MB
702.mp3 0.58 MB
78.mp3 0.58 MB
1030.mp3 0.69 MB
420.mp3 0.60 MB
1841.mp3 0.69 MB
1152.mp3 0.69 MB
1021.mp3 0.69 MB
369.mp3 0.58 MB
2056.mp3 2.56 MB
1177.mp3 0.69 MB
1673.mp3 0.69 MB
1871.mp3 0.69 MB
1370.mp3 0.69 MB
1738.mp3 0.69 MB
472.mp3 0.59 MB
1309.mp3 0.69 MB
103.mp3 0.63 MB
1525.mp3 0.69 MB
744.mp3 0.62 MB
1910.mp3 0.69 MB
1767.mp3 0.69 MB
1436.mp3 0.69 MB
216.mp3 0.54 MB
1295.mp3 0.69 MB
523.mp3 0.62 MB
1606.mp3 0.69 MB
159.mp3 0.52 MB
1262.mp3 0.69 MB
1420.mp3 0.69 MB
714.mp3 0.60 MB
46.mp3 0.61 MB
2003.mp3 3.76 MB
428.mp3 0.61 MB
1529.mp3 0.69 MB
1923.mp3 0.69 MB
512.mp3 0.55 MB
1184.mp3 0.69 MB
498.mp3 0.54 MB
1195.mp3 0.69 MB
825.mp3 0.57 MB
2034.mp3 1.71 MB
10.mp3 0.59 MB
1144.mp3 0.69 MB
1443.mp3 0.69 MB
2017.mp3 0.75 MB
1901.mp3 0.69 MB
748.mp3 0.59 MB
1103.mp3 0.69 MB
1410.mp3 0.69 MB
996.mp3 0.64 MB
1942.mp3 0.69 MB
1706.mp3 0.69 MB
1256.mp3 0.69 MB
874.mp3 0.63 MB
844.mp3 0.52 MB
1450.mp3 0.69 MB
463.mp3 0.63 MB
1047.mp3 0.69 MB
1977.mp3 0.69 MB
597.mp3 0.58 MB
643.mp3 0.56 MB
199.mp3 0.55 MB
897.mp3 0.55 MB
1423.mp3 0.69 MB
1720.mp3 0.69 MB
2029.mp3 2.61 MB
1091.mp3 0.69 MB
1145.mp3 0.69 MB
1206.mp3 0.69 MB
1039.mp3 0.69 MB
1717.mp3 0.69 MB
1490.mp3 0.69 MB
1845.mp3 0.69 MB
475.mp3 0.61 MB
1950.mp3 0.69 MB
1402.mp3 0.69 MB
790.mp3 0.62 MB
522.mp3 0.56 MB
1486.mp3 0.69 MB
1497.mp3 0.69 MB
2055.mp3 4.33 MB
140.mp3 0.51 MB
999.mp3 0.54 MB
104.mp3 0.57 MB
529.mp3 0.61 MB
86.mp3 0.60 MB
397.mp3 0.61 MB
1616.mp3 0.69 MB
660.mp3 0.53 MB
1149.mp3 0.69 MB
1020.mp3 0.69 MB
586.mp3 0.57 MB
1294.mp3 0.69 MB
727.mp3 0.48 MB
695.mp3 0.58 MB
1589.mp3 0.69 MB
1111.mp3 0.69 MB
1180.mp3 0.69 MB
201.mp3 0.45 MB
73.mp3 0.58 MB
1777.mp3 0.69 MB
891.mp3 0.61 MB
499.mp3 0.57 MB
1092.mp3 0.69 MB
1451.mp3 0.69 MB
381.mp3 0.58 MB
189.mp3 0.58 MB
1367.mp3 0.69 MB
672.mp3 0.55 MB
976.mp3 0.65 MB
399.mp3 0.60 MB
653.mp3 0.58 MB
376.mp3 0.58 MB
854.mp3 0.57 MB
342.mp3 0.53 MB
1247.mp3 0.69 MB
852.mp3 0.44 MB
148.mp3 0.63 MB
2005.mp3 3.94 MB
1926.mp3 0.69 MB
1499.mp3 0.69 MB
1909.mp3 0.69 MB
682.mp3 0.60 MB
297.mp3 0.58 MB
1156.mp3 0.69 MB
1586.mp3 0.69 MB
281.mp3 0.45 MB
314.mp3 0.57 MB
123.mp3 0.63 MB
866.mp3 0.58 MB
1362.mp3 0.69 MB
478.mp3 0.51 MB
1795.mp3 0.69 MB
1906.mp3 0.69 MB
1563.mp3 0.69 MB
2031.mp3 1.67 MB
544.mp3 0.57 MB
1157.mp3 0.69 MB
83.mp3 0.60 MB
1712.mp3 0.69 MB
1533.mp3 0.69 MB
1813.mp3 0.69 MB
1552.mp3 0.69 MB
304.mp3 0.54 MB
89.mp3 0.57 MB
1037.mp3 0.69 MB
1209.mp3 0.69 MB
815.mp3 0.60 MB
378.mp3 0.57 MB
1865.mp3 0.69 MB
2024.mp3 6.16 MB
242.mp3 0.56 MB
1564.mp3 0.69 MB
415.mp3 0.63 MB
704.mp3 0.57 MB
693.mp3 0.59 MB
130.mp3 0.46 MB
892.mp3 0.56 MB
869.mp3 0.49 MB
1286.mp3 0.35 MB
855.mp3 0.46 MB
459.mp3 0.56 MB
638.mp3 0.38 MB
310.mp3 0.57 MB
1553.mp3 0.69 MB
1469.mp3 0.69 MB
484.mp3 0.59 MB
1449.mp3 0.69 MB
886.mp3 0.57 MB
1545.mp3 0.69 MB
1787.mp3 0.69 MB
1768.mp3 0.69 MB
1512.mp3 0.69 MB
226.mp3 0.51 MB
1088.mp3 0.69 MB
79.mp3 0.59 MB
1027.mp3 0.69 MB
1902.mp3 0.69 MB
710.mp3 0.58 MB
288.mp3 0.58 MB
366.mp3 0.59 MB
1825.mp3 0.35 MB
2050.mp3 3.22 MB
2026.mp3 8.18 MB
213.mp3 0.49 MB
1179.mp3 0.69 MB
1719.mp3 0.69 MB
1167.mp3 0.69 MB
1908.mp3 0.69 MB
1888.mp3 0.69 MB
64.mp3 0.59 MB
292.mp3 0.57 MB
504.mp3 0.56 MB
502.mp3 0.59 MB
1324.mp3 0.69 MB
48.mp3 0.58 MB
503.mp3 0.78 MB
520.mp3 0.58 MB
427.mp3 0.57 MB
631.mp3 0.59 MB
2018.mp3 1.58 MB
1166.mp3 0.69 MB
1051.mp3 0.69 MB
535.mp3 0.57 MB
1924.mp3 0.69 MB
1613.mp3 0.69 MB
1883.mp3 0.69 MB
1744.mp3 0.69 MB
712.mp3 0.56 MB
1114.mp3 0.69 MB
928.mp3 0.57 MB
303.mp3 0.57 MB
1584.mp3 0.69 MB
1757.mp3 0.69 MB
55.mp3 0.57 MB
1899.mp3 0.69 MB
1011.mp3 0.69 MB
4.mp3 0.59 MB
1518.mp3 0.69 MB
1285.mp3 0.69 MB
1369.mp3 0.69 MB
468.mp3 0.46 MB
1235.mp3 0.69 MB
1652.mp3 0.69 MB
1404.mp3 0.69 MB
644.mp3 0.61 MB
831.mp3 0.64 MB
1475.mp3 0.69 MB
1059.mp3 0.69 MB
2039.mp3 3.99 MB
1015.mp3 0.69 MB
810.mp3 0.53 MB
1715.mp3 0.69 MB
1174.mp3 0.69 MB
232.mp3 0.52 MB
367.mp3 0.53 MB
1398.mp3 0.69 MB
1771.mp3 0.69 MB
688.mp3 0.51 MB
315.mp3 0.57 MB
1872.mp3 0.69 MB
1590.mp3 0.69 MB
13.mp3 0.57 MB
1542.mp3 0.69 MB
1823.mp3 0.69 MB
1371.mp3 0.69 MB
194.mp3 0.49 MB
1879.mp3 0.69 MB
1782.mp3 0.69 MB
734.mp3 0.59 MB
1099.mp3 0.69 MB
1892.mp3 0.69 MB
1278.mp3 0.69 MB
416.mp3 0.63 MB
598.mp3 0.62 MB
1471.mp3 0.69 MB
1979.mp3 0.69 MB
392.mp3 0.57 MB
1561.mp3 0.69 MB
1348.mp3 0.69 MB
1805.mp3 0.69 MB
1700.mp3 0.69 MB
5.mp3 0.56 MB
24.mp3 0.59 MB
1570.mp3 0.69 MB
1943.mp3 0.69 MB
1153.mp3 0.69 MB
155.mp3 0.55 MB
1579.mp3 0.69 MB
144.mp3 0.54 MB
1627.mp3 0.69 MB
857.mp3 0.60 MB
1866.mp3 0.35 MB
1745.mp3 0.69 MB
462.mp3 0.63 MB
507.mp3 0.58 MB
1253.mp3 0.69 MB
125.mp3 0.58 MB
513.mp3 0.46 MB
616.mp3 0.57 MB
1025.mp3 0.69 MB
387.mp3 0.53 MB
1917.mp3 0.69 MB
1336.mp3 0.69 MB
730.mp3 0.58 MB
1932.mp3 0.69 MB
1308.mp3 0.69 MB
1186.mp3 0.69 MB
1135.mp3 0.69 MB
1624.mp3 0.69 MB
1258.mp3 0.69 MB
1016.mp3 0.69 MB
1526.mp3 0.69 MB
668.mp3 0.60 MB
1740.mp3 0.69 MB
1252.mp3 0.69 MB
1657.mp3 0.69 MB
1273.mp3 0.69 MB
1374.mp3 0.69 MB
974.mp3 0.67 MB
1507.mp3 0.69 MB
1543.mp3 0.69 MB
1928.mp3 0.69 MB
1877.mp3 0.69 MB
879.mp3 0.61 MB
1814.mp3 0.69 MB
1388.mp3 0.69 MB
1019.mp3 0.69 MB
622.mp3 0.58 MB
1467.mp3 0.69 MB
1708.mp3 0.35 MB
2038.mp3 3.68 MB
486.mp3 0.57 MB
1485.mp3 0.69 MB
322.mp3 0.53 MB
1647.mp3 0.69 MB
1536.mp3 0.69 MB
174.mp3 0.53 MB
608.mp3 0.46 MB
158.mp3 0.45 MB
1472.mp3 0.69 MB
1534.mp3 0.69 MB
696.mp3 0.55 MB
1205.mp3 0.69 MB
1272.mp3 0.69 MB
391.mp3 0.60 MB
743.mp3 0.60 MB
1935.mp3 0.69 MB
1126.mp3 0.69 MB
811.mp3 0.49 MB
799.mp3 0.59 MB
1539.mp3 0.69 MB
1322.mp3 0.69 MB
1806.mp3 0.69 MB
1655.mp3 0.69 MB
494.mp3 0.55 MB
1582.mp3 0.69 MB
1651.mp3 0.69 MB
1438.mp3 0.69 MB
1113.mp3 0.69 MB
1625.mp3 0.69 MB
151.mp3 0.57 MB
1054.mp3 0.69 MB
1850.mp3 0.69 MB
2027.mp3 3.47 MB
67.mp3 0.59 MB
1585.mp3 0.69 MB
1723.mp3 0.69 MB
2023.mp3 3.64 MB
723.mp3 0.62 MB
1320.mp3 0.69 MB
1284.mp3 0.69 MB
796.mp3 0.58 MB
299.mp3 0.54 MB
1611.mp3 0.69 MB
1665.mp3 0.69 MB
1143.mp3 0.69 MB
500.mp3 0.57 MB
1964.mp3 0.69 MB
186.mp3 0.55 MB
818.mp3 0.60 MB
1967.mp3 0.69 MB
1687.mp3 0.69 MB
1296.mp3 0.69 MB
1755.mp3 0.69 MB
145.mp3 0.52 MB
1889.mp3 0.69 MB
1498.mp3 0.69 MB
2009.mp3 3.59 MB
864.mp3 0.56 MB
1614.mp3 0.69 MB
1458.mp3 0.69 MB
357.mp3 0.59 MB
1750.mp3 0.69 MB
177.mp3 0.59 MB
335.mp3 0.58 MB
1129.mp3 0.69 MB
993.mp3 0.60 MB
1995.mp3 0.69 MB
1399.mp3 0.69 MB
102.mp3 0.61 MB
1382.mp3 0.69 MB
40.mp3 0.57 MB
1356.mp3 0.69 MB
747.mp3 0.48 MB
1081.mp3 0.69 MB
346.mp3 0.61 MB
1168.mp3 0.69 MB
166.mp3 0.56 MB
724.mp3 0.58 MB
1861.mp3 0.69 MB
131.mp3 0.58 MB
939.mp3 0.62 MB
168.mp3 0.53 MB
1346.mp3 0.69 MB
327.mp3 0.58 MB
525.mp3 0.53 MB
1483.mp3 0.69 MB
41.mp3 0.59 MB
157.mp3 0.54 MB
580.mp3 0.53 MB
1012.mp3 0.69 MB
1066.mp3 0.69 MB
386.mp3 0.59 MB
1765.mp3 0.69 MB
223.mp3 0.43 MB
581.mp3 0.59 MB
348.mp3 0.58 MB
564.mp3 0.59 MB
1937.mp3 0.69 MB
942.mp3 0.56 MB
1863.mp3 0.69 MB
1513.mp3 0.69 MB
1527.mp3 0.69 MB
1463.mp3 0.69 MB
1084.mp3 0.69 MB
405.mp3 0.60 MB
639.mp3 0.59 MB
1597.mp3 0.69 MB
977.mp3 0.62 MB
1263.mp3 0.69 MB
887.mp3 0.65 MB
289.mp3 0.40 MB
1407.mp3 0.69 MB
68.mp3 0.58 MB
1076.mp3 0.69 MB
746.mp3 0.60 MB
719.mp3 0.59 MB
340.mp3 0.60 MB
2006.mp3 2.52 MB
1002.mp3 0.69 MB
241.mp3 0.55 MB
441.mp3 0.58 MB
250.mp3 0.54 MB
577.mp3 0.55 MB
628.mp3 0.59 MB
1934.mp3 0.69 MB
91.mp3 0.57 MB
1010.mp3 0.69 MB
740.mp3 0.57 MB
1401.mp3 0.69 MB
1210.mp3 0.69 MB
1699.mp3 0.69 MB
442.mp3 0.58 MB
1226.mp3 0.69 MB
1827.mp3 0.69 MB
1619.mp3 0.69 MB
991.mp3 0.54 MB
323.mp3 0.59 MB
1846.mp3 0.69 MB
344.mp3 0.58 MB
1959.mp3 0.69 MB
25.mp3 0.59 MB
1307.mp3 0.69 MB
219.mp3 0.57 MB
605.mp3 0.60 MB
1986.mp3 0.69 MB
1298.mp3 0.69 MB
1698.mp3 0.69 MB
430.mp3 0.57 MB
658.mp3 0.60 MB
196.mp3 0.54 MB
1433.mp3 0.69 MB
1580.mp3 0.69 MB
20.mp3 0.61 MB
945.mp3 0.56 MB
1452.mp3 0.69 MB
623.mp3 0.56 MB
1984.mp3 0.69 MB
1811.mp3 0.69 MB
1288.mp3 0.69 MB
934.mp3 0.58 MB
142.mp3 0.47 MB
173.mp3 0.54 MB
1340.mp3 0.69 MB
650.mp3 0.59 MB
309.mp3 0.60 MB
1364.mp3 0.69 MB
1335.mp3 0.69 MB
1415.mp3 0.69 MB
1124.mp3 0.69 MB
1707.mp3 0.69 MB
1244.mp3 0.69 MB
537.mp3 0.57 MB
1623.mp3 0.69 MB
1338.mp3 0.69 MB
1279.mp3 0.69 MB
1479.mp3 0.69 MB
2025.mp3 6.85 MB
1267.mp3 0.69 MB
1911.mp3 0.69 MB
197.mp3 0.43 MB
1973.mp3 0.69 MB
1874.mp3 0.69 MB
1133.mp3 0.69 MB
192.mp3 0.59 MB
2045.mp3 1.35 MB
1759.mp3 0.69 MB
711.mp3 0.53 MB
1760.mp3 0.69 MB
1958.mp3 0.69 MB
1729.mp3 0.69 MB
1875.mp3 0.69 MB
1634.mp3 0.69 MB
1349.mp3 0.69 MB
1464.mp3 0.69 MB
1035.mp3 0.69 MB
1095.mp3 0.69 MB
270.mp3 0.43 MB
1050.mp3 0.69 MB
1121.mp3 0.69 MB
1194.mp3 0.69 MB
1893.mp3 0.69 MB
1022.mp3 0.69 MB
1978.mp3 0.69 MB
1920.mp3 0.69 MB
318.mp3 0.64 MB
425.mp3 0.60 MB
1638.mp3 0.69 MB
1183.mp3 0.69 MB
1626.mp3 0.69 MB
547.mp3 0.56 MB
1675.mp3 0.69 MB
691.mp3 0.60 MB
651.mp3 0.58 MB
1820.mp3 0.69 MB
1014.mp3 0.69 MB
1236.mp3 0.69 MB
2007.mp3 4.45 MB
1476.mp3 0.69 MB
1998.mp3 0.69 MB
127.mp3 0.41 MB
1193.mp3 0.69 MB
647.mp3 0.59 MB
709.mp3 0.60 MB
1509.mp3 0.69 MB
858.mp3 0.55 MB
1283.mp3 0.69 MB
1077.mp3 0.69 MB
17.mp3 0.60 MB
2046.mp3 4.33 MB
1350.mp3 0.69 MB
1839.mp3 0.69 MB
128.mp3 0.48 MB
445.mp3 0.44 MB
1062.mp3 0.69 MB
1523.mp3 0.69 MB
2021.mp3 4.79 MB
1569.mp3 0.69 MB
1598.mp3 0.69 MB
777.mp3 0.59 MB
2020.mp3 2.89 MB
970.mp3 0.61 MB
2016.mp3 0.87 MB
1045.mp3 0.69 MB
1473.mp3 0.69 MB
1023.mp3 0.69 MB
1764.mp3 0.69 MB
1386.mp3 0.69 MB
1297.mp3 0.69 MB
1373.mp3 0.35 MB
1040.mp3 0.69 MB
1110.mp3 0.69 MB
203.mp3 0.49 MB
290.mp3 0.50 MB
1796.mp3 0.69 MB
1982.mp3 0.69 MB
8.mp3 0.57 MB
1873.mp3 0.69 MB
1680.mp3 0.69 MB
1524.mp3 0.69 MB
737.mp3 0.55 MB
1887.mp3 0.69 MB
390.mp3 0.60 MB
1354.mp3 0.69 MB
449.mp3 0.52 MB
1127.mp3 0.69 MB
1254.mp3 0.69 MB
807.mp3 0.62 MB
1117.mp3 0.69 MB
1106.mp3 0.69 MB
257.mp3 0.57 MB
136.mp3 0.60 MB
496.mp3 0.60 MB
910.mp3 0.62 MB
1319.mp3 0.69 MB
1302.mp3 0.69 MB
1429.mp3 0.69 MB
1001.mp3 0.69 MB
1191.mp3 0.69 MB
904.mp3 0.57 MB
1557.mp3 0.69 MB
286.mp3 0.48 MB
2015.mp3 2.64 MB
1617.mp3 0.69 MB
1754.mp3 0.69 MB
80.mp3 0.61 MB
1026.mp3 0.69 MB
96.mp3 0.62 MB
1028.mp3 0.69 MB
1856.mp3 0.69 MB
400.mp3 0.59 MB
182.mp3 0.50 MB
129.mp3 0.62 MB
958.mp3 0.58 MB
1078.mp3 0.69 MB
1676.mp3 0.69 MB
1810.mp3 0.69 MB
2004.mp3 2.97 MB
294.mp3 0.42 MB
1087.mp3 0.69 MB
1724.mp3 0.69 MB
1192.mp3 0.69 MB
978.mp3 0.59 MB
1229.mp3 0.69 MB
92.mp3 0.58 MB
1159.mp3 0.69 MB
750.mp3 0.52 MB
448.mp3 0.60 MB
1997.mp3 0.69 MB
1119.mp3 0.69 MB
1240.mp3 0.69 MB
1852.mp3 0.69 MB
1508.mp3 0.69 MB
1214.mp3 0.69 MB
275.mp3 0.57 MB
1109.mp3 0.69 MB
1150.mp3 0.69 MB
312.mp3 0.57 MB
440.mp3 0.60 MB
220.mp3 0.50 MB
1558.mp3 0.69 MB
1280.mp3 0.69 MB
518.mp3 0.56 MB
1421.mp3 0.69 MB
74.mp3 0.62 MB
162.mp3 0.46 MB
823.mp3 0.57 MB
205.mp3 0.46 MB
1849.mp3 0.69 MB
1803.mp3 0.69 MB
85.mp3 0.59 MB
637.mp3 0.47 MB
1178.mp3 0.69 MB
1956.mp3 0.69 MB
410.mp3 0.58 MB
994.mp3 0.65 MB
767.mp3 0.60 MB
480.mp3 0.46 MB
146.mp3 0.46 MB
349.mp3 0.57 MB
1922.mp3 0.69 MB
769.mp3 0.61 MB
1207.mp3 0.69 MB
236.mp3 0.52 MB
1448.mp3 0.69 MB
383.mp3 0.56 MB
1289.mp3 0.69 MB
1466.mp3 0.69 MB
1550.mp3 0.69 MB
1568.mp3 0.69 MB
1772.mp3 0.69 MB
731.mp3 0.58 MB
1501.mp3 0.69 MB
1128.mp3 0.69 MB
453.mp3 0.60 MB
741.mp3 0.58 MB
600.mp3 0.56 MB
1389.mp3 0.69 MB
356.mp3 0.62 MB
469.mp3 0.61 MB
278.mp3 0.55 MB
1743.mp3 0.69 MB
1621.mp3 0.69 MB
882.mp3 0.59 MB
921.mp3 0.58 MB
2001.mp3 4.60 MB
284.mp3 0.57 MB
1832.mp3 0.69 MB
135.mp3 0.53 MB
225.mp3 0.50 MB
952.mp3 0.52 MB
663.mp3 0.62 MB
763.mp3 0.58 MB
860.mp3 0.58 MB
1603.mp3 0.69 MB
298.mp3 0.57 MB
1596.mp3 0.69 MB
1559.mp3 0.69 MB
1696.mp3 0.69 MB
308.mp3 0.58 MB
1965.mp3 0.69 MB
1136.mp3 0.69 MB
673.mp3 0.59 MB
1504.mp3 0.69 MB
1392.mp3 0.69 MB
1890.mp3 0.69 MB
401.mp3 0.62 MB
1208.mp3 0.69 MB
1735.mp3 0.69 MB
1783.mp3 0.69 MB
1142.mp3 0.69 MB
1648.mp3 0.69 MB
749.mp3 0.61 MB
338.mp3 0.55 MB
1671.mp3 0.69 MB
1544.mp3 0.69 MB
1615.mp3 0.69 MB
273.mp3 0.57 MB
1878.mp3 0.69 MB
530.mp3 0.58 MB
1123.mp3 0.69 MB
1912.mp3 0.69 MB
1987.mp3 0.69 MB
1830.mp3 0.69 MB
1818.mp3 0.69 MB
1528.mp3 0.69 MB
938.mp3 0.58 MB
124.mp3 0.58 MB
1881.mp3 0.69 MB
426.mp3 0.59 MB
1503.mp3 0.69 MB
1187.mp3 0.69 MB
114.mp3 0.58 MB
867.mp3 0.62 MB
1417.mp3 0.69 MB
1108.mp3 0.69 MB
1702.mp3 0.69 MB
1495.mp3 0.69 MB
632.mp3 0.58 MB
1326.mp3 0.69 MB
1385.mp3 0.69 MB
911.mp3 0.59 MB
1330.mp3 0.69 MB
1029.mp3 0.69 MB
1445.mp3 0.69 MB
1489.mp3 0.69 MB
1182.mp3 0.69 MB
198.mp3 0.54 MB
419.mp3 0.49 MB
1434.mp3 0.69 MB
121.mp3 0.58 MB
1242.mp3 0.69 MB
1694.mp3 0.69 MB
995.mp3 0.58 MB
1867.mp3 0.35 MB
1833.mp3 0.69 MB
1102.mp3 0.69 MB
800.mp3 0.58 MB
1203.mp3 0.35 MB
1788.mp3 0.69 MB
1300.mp3 0.69 MB
454.mp3 0.61 MB
1017.mp3 0.69 MB
1259.mp3 0.69 MB
1277.mp3 0.69 MB
1082.mp3 0.69 MB
671.mp3 0.53 MB
71.mp3 0.61 MB
890.mp3 0.56 MB
514.mp3 0.59 MB
320.mp3 0.57 MB
1196.mp3 0.69 MB
360.mp3 0.55 MB
667.mp3 0.57 MB
782.mp3 0.59 MB
829.mp3 0.58 MB
565.mp3 0.62 MB
560.mp3 0.53 MB
1089.mp3 0.69 MB
1439.mp3 0.69 MB
1588.mp3 0.69 MB
12.mp3 0.58 MB
1884.mp3 0.69 MB
1003.mp3 0.69 MB
634.mp3 0.59 MB
1921.mp3 0.69 MB
736.mp3 0.47 MB
846.mp3 0.60 MB
556.mp3 0.55 MB
477.mp3 0.65 MB
1854.mp3 0.69 MB
689.mp3 0.60 MB
666.mp3 0.59 MB
980.mp3 0.61 MB
1571.mp3 0.69 MB
1510.mp3 0.69 MB
1858.mp3 0.69 MB
2030.mp3 4.20 MB
72.mp3 0.59 MB
95.mp3 0.54 MB
1692.mp3 0.69 MB
1104.mp3 0.69 MB
1230.mp3 0.35 MB
568.mp3 0.52 MB
726.mp3 0.57 MB
422.mp3 0.56 MB
1343.mp3 0.69 MB
1843.mp3 0.69 MB
316.mp3 0.59 MB
49.mp3 0.59 MB
1886.mp3 0.69 MB
266.mp3 0.57 MB
1594.mp3 0.69 MB
1972.mp3 0.69 MB
797.mp3 0.57 MB
109.mp3 0.61 MB
859.mp3 0.61 MB
585.mp3 0.59 MB
1566.mp3 0.69 MB
117.mp3 0.52 MB
1260.mp3 0.69 MB
1352.mp3 0.69 MB
501.mp3 0.51 MB
1855.mp3 0.69 MB
889.mp3 0.59 MB
851.mp3 0.62 MB
1918.mp3 0.69 MB
224.mp3 0.60 MB
1005.mp3 0.69 MB
1916.mp3 0.69 MB
1345.mp3 0.69 MB
1517.mp3 0.69 MB
805.mp3 0.58 MB
1494.mp3 0.69 MB
1218.mp3 0.35 MB
1685.mp3 0.69 MB
1055.mp3 0.69 MB
733.mp3 0.56 MB
1036.mp3 0.69 MB
700.mp3 0.58 MB
987.mp3 0.65 MB
436.mp3 0.58 MB
2043.mp3 4.15 MB
1048.mp3 0.69 MB
1639.mp3 0.69 MB
1422.mp3 0.69 MB
1215.mp3 0.69 MB
1231.mp3 0.69 MB
1327.mp3 0.69 MB
1057.mp3 0.69 MB
1112.mp3 0.69 MB
1703.mp3 0.69 MB
238.mp3 0.46 MB
244.mp3 0.58 MB
1060.mp3 0.69 MB
1530.mp3 0.69 MB
1257.mp3 0.69 MB
1847.mp3 0.69 MB
1225.mp3 0.69 MB
1491.mp3 0.69 MB
1377.mp3 0.69 MB
1609.mp3 0.69 MB
1949.mp3 0.69 MB
1725.mp3 0.69 MB
1317.mp3 0.69 MB
1895.mp3 0.69 MB
1107.mp3 0.69 MB
1575.mp3 0.69 MB
1790.mp3 0.69 MB
1470.mp3 0.69 MB
1232.mp3 0.69 MB
1683.mp3 0.69 MB
1860.mp3 0.69 MB
871.mp3 0.61 MB
1983.mp3 0.69 MB
1548.mp3 0.69 MB
833.mp3 0.60 MB
708.mp3 0.53 MB
233.mp3 0.57 MB
967.mp3 0.53 MB
1641.mp3 0.69 MB
676.mp3 0.59 MB
1809.mp3 0.69 MB
1730.mp3 0.69 MB
1733.mp3 0.69 MB
1996.mp3 0.69 MB
1876.mp3 0.69 MB
1282.mp3 0.69 MB
1216.mp3 0.69 MB
1643.mp3 0.69 MB
1992.mp3 0.69 MB
1474.mp3 0.69 MB
555.mp3 0.61 MB
1217.mp3 0.69 MB
1880.mp3 0.69 MB
481.mp3 0.54 MB
956.mp3 0.60 MB
1437.mp3 0.69 MB
272.mp3 0.58 MB
664.mp3 0.59 MB
621.mp3 0.54 MB
444.mp3 0.58 MB
330.mp3 0.65 MB
460.mp3 0.55 MB
1359.mp3 0.69 MB
143.mp3 0.45 MB
960.mp3 0.60 MB
193.mp3 0.57 MB
661.mp3 0.57 MB
1546.mp3 0.69 MB
408.mp3 0.59 MB
1199.mp3 0.69 MB
229.mp3 0.60 MB
1249.mp3 0.69 MB
1751.mp3 0.69 MB
1637.mp3 0.69 MB
1822.mp3 0.69 MB
1160.mp3 0.69 MB
675.mp3 0.60 MB
98.mp3 0.66 MB
2036.mp3 4.47 MB
1662.mp3 0.69 MB
118.mp3 0.61 MB
1380.mp3 0.69 MB
279.mp3 0.55 MB
32.mp3 0.66 MB
2012.mp3 5.52 MB
1344.mp3 0.69 MB
997.mp3 0.66 MB
172.mp3 0.51 MB
1669.mp3 0.69 MB
1610.mp3 0.69 MB
1310.mp3 0.69 MB
1224.mp3 0.69 MB
485.mp3 0.45 MB
1870.mp3 0.69 MB
82.mp3 0.57 MB
1785.mp3 0.69 MB
1944.mp3 0.69 MB
379.mp3 0.53 MB
824.mp3 0.64 MB
1868.mp3 0.69 MB
1313.mp3 0.69 MB
739.mp3 0.59 MB
931.mp3 0.60 MB
1592.mp3 0.69 MB
37.mp3 0.60 MB
1264.mp3 0.69 MB
321.mp3 0.64 MB
1824.mp3 0.69 MB
1379.mp3 0.69 MB
260.mp3 0.58 MB
431.mp3 0.62 MB
248.mp3 0.58 MB
784.mp3 0.64 MB
2044.mp3 2.30 MB
1515.mp3 0.69 MB
988.mp3 0.60 MB
1004.mp3 0.69 MB
488.mp3 0.56 MB
1907.mp3 0.69 MB
2054.mp3 1.46 MB
358.mp3 0.60 MB
1046.mp3 0.69 MB
59.mp3 0.66 MB
1444.mp3 0.69 MB
1522.mp3 0.69 MB
1791.mp3 0.69 MB
1812.mp3 0.69 MB
352.mp3 0.59 MB
872.mp3 0.48 MB
88.mp3 0.59 MB
384.mp3 0.60 MB
936.mp3 0.59 MB
1457.mp3 0.69 MB
1808.mp3 0.69 MB
1537.mp3 0.69 MB
1170.mp3 0.69 MB
1631.mp3 0.69 MB
791.mp3 0.40 MB
1684.mp3 0.69 MB
227.mp3 0.52 MB
1454.mp3 0.69 MB
873.mp3 0.62 MB
1381.mp3 0.69 MB
990.mp3 0.50 MB
662.mp3 0.58 MB
1481.mp3 0.69 MB
339.mp3 0.61 MB
1424.mp3 0.69 MB
674.mp3 0.59 MB
1746.mp3 0.69 MB
333.mp3 0.57 MB
150.mp3 0.55 MB
1493.mp3 0.69 MB
1355.mp3 0.69 MB
1773.mp3 0.69 MB
19.mp3 0.59 MB
1804.mp3 0.69 MB
1990.mp3 0.69 MB
1800.mp3 0.69 MB
646.mp3 0.49 MB
1929.mp3 0.69 MB
215.mp3 0.51 MB
455.mp3 0.61 MB
1484.mp3 0.69 MB
171.mp3 0.43 MB
1885.mp3 0.69 MB
1595.mp3 0.69 MB
1190.mp3 0.69 MB
404.mp3 0.70 MB
1440.mp3 0.69 MB
1311.mp3 0.69 MB
217.mp3 0.54 MB
1482.mp3 0.69 MB
1321.mp3 0.69 MB
90.mp3 0.57 MB
113.mp3 0.61 MB
1857.mp3 0.69 MB
1405.mp3 0.69 MB
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In [6]:
%%capture
!pip install librosa
In [7]:
import librosa
import glob
audio_dir = os.path.join(dataset_path, "DEAM_audio", "MEMD_audio")
mp3_files = glob.glob(os.path.join(audio_dir, "*.mp3"))
print(f"Found {len(mp3_files)} MP3 files")
if mp3_files:
audio_file = mp3_files[0]
y, sr = librosa.load(audio_file)
print("🎧 Sample rate:", sr)
print("🎵 Duration (seconds):", len(y) / sr)
print("📐 Shape:", y.shape)
print("📂 File path:", audio_file)
Found 1802 MP3 files 🎧 Sample rate: 22050 🎵 Duration (seconds): 45.08734693877551 📐 Shape: (994176,) 📂 File path: /root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_audio/MEMD_audio/1739.mp3
In [8]:
csv_files = glob.glob(os.path.join(dataset_path, "**/*.csv"), recursive=True)
print(csv_files)
['/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1910.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1834.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1524.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1220.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1231.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1350.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1816.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/530.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/83.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1212.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/962.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/844.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1124.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1924.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/359.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/764.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/177.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1664.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/617.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/865.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/749.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/629.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/392.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/344.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1667.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/791.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1981.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1193.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/169.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1356.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1000.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1085.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1020.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/415.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/487.csv', '/root/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1656.csv', 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In [9]:
import pandas as pd
df = pd.read_csv(csv_files[0])
print("🧾 Shape:", df.shape)
print("📋 Columns:", df.columns)
print(df.head())
🧾 Shape: (10, 61)
📋 Columns: Index(['WorkerId', 'sample_15000ms', 'sample_15500ms', 'sample_16000ms',
'sample_16500ms', 'sample_17000ms', 'sample_17500ms', 'sample_18000ms',
'sample_18500ms', 'sample_19000ms', 'sample_19500ms', 'sample_20000ms',
'sample_20500ms', 'sample_21000ms', 'sample_21500ms', 'sample_22000ms',
'sample_22500ms', 'sample_23000ms', 'sample_23500ms', 'sample_24000ms',
'sample_24500ms', 'sample_25000ms', 'sample_25500ms', 'sample_26000ms',
'sample_26500ms', 'sample_27000ms', 'sample_27500ms', 'sample_28000ms',
'sample_28500ms', 'sample_29000ms', 'sample_29500ms', 'sample_30000ms',
'sample_30500ms', 'sample_31000ms', 'sample_31500ms', 'sample_32000ms',
'sample_32500ms', 'sample_33000ms', 'sample_33500ms', 'sample_34000ms',
'sample_34500ms', 'sample_35000ms', 'sample_35500ms', 'sample_36000ms',
'sample_36500ms', 'sample_37000ms', 'sample_37500ms', 'sample_38000ms',
'sample_38500ms', 'sample_39000ms', 'sample_39500ms', 'sample_40000ms',
'sample_40500ms', 'sample_41000ms', 'sample_41500ms', 'sample_42000ms',
'sample_42500ms', 'sample_43000ms', 'sample_43500ms', 'sample_44000ms',
'sample_44500ms'],
dtype='object')
WorkerId sample_15000ms sample_15500ms \
0 bb50b45a1874ede476874bd57e4cabb4 -0.16 -0.32
1 ff9c1993d2a21f2117c30d8e295dd4ac -0.26 -0.26
2 78b5e9744073532cc376976b5fc6b2fc -0.26 -0.26
3 00de940f0b5cfc82cca4791199e3bfb3 0.29 0.29
4 de2b2c35312ac2f0a8510743742c0219 0.10 0.12
sample_16000ms sample_16500ms sample_17000ms sample_17500ms \
0 -0.22 -0.20 -0.19 -0.14
1 -0.26 -0.26 -0.26 -0.26
2 -0.26 -0.26 0.03 0.30
3 0.29 0.29 0.29 0.29
4 0.11 0.12 0.10 0.11
sample_18000ms sample_18500ms sample_19000ms ... sample_40000ms \
0 -0.22 -0.23 -0.17 ... -0.08
1 -0.26 -0.26 -0.26 ... -0.26
2 0.30 0.30 0.30 ... 0.01
3 0.29 0.29 0.29 ... 0.29
4 0.11 0.10 0.12 ... 0.12
sample_40500ms sample_41000ms sample_41500ms sample_42000ms \
0 -0.05 -0.05 -0.04 -0.10
1 -0.26 -0.26 -0.26 -0.26
2 0.01 0.01 0.01 0.01
3 0.29 0.29 0.29 0.29
4 0.11 0.14 0.11 0.12
sample_42500ms sample_43000ms sample_43500ms sample_44000ms \
0 -0.10 -0.05 -0.17 -0.13
1 -0.26 -0.26 -0.26 -0.26
2 0.01 0.01 0.01 0.01
3 0.29 0.29 0.29 0.29
4 0.11 0.12 0.13 0.13
sample_44500ms
0 -0.16
1 -0.26
2 0.01
3 0.29
4 0.13
[5 rows x 61 columns]
Below we look at the Arousal, Valence and Static annotations files.¶
In [10]:
valence_path = os.path.join(dataset_path, "DEAM_Annotations", "annotations", "annotations averaged per song", "dynamic (per second annotations)", "valence.csv")
arousal_path = os.path.join(dataset_path, "DEAM_Annotations", "annotations", "annotations averaged per song", "dynamic (per second annotations)", "arousal.csv")
static_1_path = os.path.join(dataset_path, "DEAM_Annotations", "annotations", "annotations averaged per song", "song_level", "static_annotations_averaged_songs_1_2000.csv")
static_2_path = os.path.join(dataset_path, "DEAM_Annotations", "annotations", "annotations averaged per song", "song_level", "static_annotations_averaged_songs_2000_2058.csv")
In [11]:
# Load and show head for each
valence_df = pd.read_csv(valence_path)
arousal_df = pd.read_csv(arousal_path)
static_1_df = pd.read_csv(static_1_path)
static_2_df = pd.read_csv(static_2_path)
Valence¶
File contains second-by-second (time-series) average valence ratings for each song, indicating how emotionally positive or negative the music is over time. 0 is sad, 1 is happy.
In [12]:
print(" Valence (dynamic, per second):")
display(valence_df.head())
valence_df.describe()
Valence (dynamic, per second):
| song_id | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_621500ms | sample_622000ms | sample_622500ms | sample_623000ms | sample_623500ms | sample_624000ms | sample_624500ms | sample_625000ms | sample_625500ms | sample_626000ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | -0.073341 | -0.074661 | -0.074077 | -0.078154 | -0.081588 | -0.080873 | -0.083611 | -0.082145 | -0.081632 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 3 | -0.189702 | -0.187765 | -0.183740 | -0.188761 | -0.193704 | -0.190834 | -0.189041 | -0.197554 | -0.202871 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 4 | 0.154286 | 0.148063 | 0.146956 | 0.151180 | 0.135942 | 0.076832 | 0.084487 | 0.088513 | 0.090944 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 5 | 0.149053 | 0.148260 | 0.147369 | 0.146956 | 0.147857 | 0.146927 | 0.145651 | 0.145795 | 0.146420 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 7 | 0.342816 | 0.345193 | 0.332023 | 0.328887 | 0.327603 | 0.329011 | 0.327883 | 0.334822 | 0.328447 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 1224 columns
Out[12]:
| song_id | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_621500ms | sample_622000ms | sample_622500ms | sample_623000ms | sample_623500ms | sample_624000ms | sample_624500ms | sample_625000ms | sample_625500ms | sample_626000ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | ... | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1.00 | 1.000 | 1.00 | 1.000 | 1.000 |
| mean | 1093.215871 | 0.100265 | 0.100442 | 0.100808 | 0.100297 | 0.100203 | 0.099390 | 0.099205 | 0.099683 | 0.099590 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
| std | 599.658004 | 0.210479 | 0.211540 | 0.212936 | 0.213369 | 0.215074 | 0.216388 | 0.217358 | 0.218508 | 0.219200 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| min | 2.000000 | -0.581823 | -0.584132 | -0.593335 | -0.593696 | -0.595547 | -0.600594 | -0.611527 | -0.613262 | -0.615290 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
| 25% | 569.500000 | -0.048420 | -0.045530 | -0.046436 | -0.047475 | -0.048000 | -0.048000 | -0.050986 | -0.049500 | -0.050868 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
| 50% | 1157.500000 | 0.105500 | 0.105630 | 0.107000 | 0.105000 | 0.104594 | 0.103500 | 0.103448 | 0.100500 | 0.103000 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
| 75% | 1607.750000 | 0.254341 | 0.255000 | 0.258975 | 0.259000 | 0.263948 | 0.261392 | 0.264000 | 0.267750 | 0.266049 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
| max | 2058.000000 | 0.614000 | 0.601000 | 0.600000 | 0.614000 | 0.627000 | 0.641000 | 0.634000 | 0.611000 | 0.636000 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
8 rows × 1224 columns
Arousal file¶
Contains second-by-second (time-series) average arousal ratings for each song, reflecting the intensity or energy level of the music over time. 0 is calm, 1 is ubpeat.
In [13]:
print("\n Arousal (dynamic, per second):")
display(arousal_df.head())
arousal_df.describe()
Arousal (dynamic, per second):
| song_id | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_622000ms | sample_622500ms | sample_623000ms | sample_623500ms | sample_624000ms | sample_624500ms | sample_625000ms | sample_625500ms | sample_626000ms | sample_626500ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | -0.109386 | -0.114942 | -0.116413 | -0.118613 | -0.126457 | -0.133199 | -0.136855 | -0.144713 | -0.138985 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 3 | -0.110846 | -0.123973 | -0.131103 | -0.135956 | -0.140775 | -0.144664 | -0.163118 | -0.165218 | -0.158858 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 4 | 0.222327 | 0.179446 | 0.178388 | 0.184056 | 0.176042 | 0.178720 | 0.176345 | 0.175793 | 0.176154 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 5 | -0.255613 | -0.251579 | -0.251958 | -0.251124 | -0.250763 | -0.251957 | -0.251957 | -0.251957 | -0.251957 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 7 | 0.464234 | 0.460789 | 0.460991 | 0.461046 | 0.457240 | 0.465702 | 0.471809 | 0.469918 | 0.473377 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 1225 columns
Out[13]:
| song_id | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_622000ms | sample_622500ms | sample_623000ms | sample_623500ms | sample_624000ms | sample_624500ms | sample_625000ms | sample_625500ms | sample_626000ms | sample_626500ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | ... | 1.00 | 1.00 | 1.000 | 1.00 | 1.000 | 1.000 | 1.00 | 1.000 | 1.00 | 1.000 |
| mean | 1093.215871 | 0.123961 | 0.124999 | 0.125694 | 0.126370 | 0.126515 | 0.126578 | 0.126825 | 0.127375 | 0.127409 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
| std | 599.658004 | 0.266756 | 0.267682 | 0.268578 | 0.269261 | 0.269770 | 0.270248 | 0.270959 | 0.272065 | 0.272942 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| min | 2.000000 | -0.654305 | -0.654348 | -0.655628 | -0.663082 | -0.664821 | -0.665411 | -0.667466 | -0.668622 | -0.661271 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
| 25% | 569.500000 | -0.071864 | -0.070000 | -0.072250 | -0.072093 | -0.073750 | -0.069962 | -0.076000 | -0.076922 | -0.077000 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
| 50% | 1157.500000 | 0.139000 | 0.140830 | 0.144438 | 0.144235 | 0.143667 | 0.140169 | 0.141000 | 0.142247 | 0.142000 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
| 75% | 1607.750000 | 0.341000 | 0.341581 | 0.343351 | 0.344000 | 0.343506 | 0.344971 | 0.346750 | 0.348424 | 0.350978 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
| max | 2058.000000 | 0.678000 | 0.687000 | 0.688000 | 0.696000 | 0.693000 | 0.688000 | 0.695000 | 0.698000 | 0.702000 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
8 rows × 1225 columns
static_annotations_averaged_songs_1_2000.csv¶
Provides overall mean and standard deviation values for valence and arousal for each of the first 2000 songs, summarizing their emotional content across time and raters.
In [14]:
print("\n Static Annotations (1–2000):")
display(static_1_df.head())
static_1_df.describe()
Static Annotations (1–2000):
| song_id | valence_mean | valence_std | arousal_mean | arousal_std | |
|---|---|---|---|---|---|
| 0 | 2 | 3.1 | 0.94 | 3.0 | 0.63 |
| 1 | 3 | 3.5 | 1.75 | 3.3 | 1.62 |
| 2 | 4 | 5.7 | 1.42 | 5.5 | 1.63 |
| 3 | 5 | 4.4 | 2.01 | 5.3 | 1.85 |
| 4 | 7 | 5.8 | 1.47 | 6.4 | 1.69 |
Out[14]:
| song_id | valence_mean | valence_std | arousal_mean | arousal_std | |
|---|---|---|---|---|---|
| count | 1744.000000 | 1744.000000 | 1744.000000 | 1744.000000 | 1744.000000 |
| mean | 1062.077982 | 4.903492 | 1.521950 | 4.812603 | 1.466778 |
| std | 584.299005 | 1.173920 | 0.385496 | 1.289368 | 0.367197 |
| min | 2.000000 | 1.600000 | 0.300000 | 1.600000 | 0.460000 |
| 25% | 536.750000 | 4.100000 | 1.270000 | 3.800000 | 1.200000 |
| 50% | 1128.500000 | 4.900000 | 1.510000 | 4.900000 | 1.480000 |
| 75% | 1564.250000 | 5.800000 | 1.780000 | 5.800000 | 1.720000 |
| max | 2000.000000 | 8.400000 | 2.900000 | 8.100000 | 2.590000 |
static_annotations_averaged_songs_2000_2058.csv¶
Provides extended emotional statistics—including mean, max, and min values with standard deviations—for valence and arousal for songs 2000 to 2058, offering a more detailed summary of their emotional dynamics.
In [15]:
print("\n Static Annotations (2000–2058):")
display(static_2_df.head())
static_2_df.describe()
Static Annotations (2000–2058):
| song_id | valence_mean | valence_std | valence_ max_mean | valence_max_std | valence_min_mean | valence_min_std | arousal_mean | arousal_std | arousal_max_mean | arousal_max_std | arousal_min_mean | arousal_min_std | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2001 | 3.2 | 0.98 | 5.0 | 1.41 | 2.2 | 0.98 | 6.6 | 0.80 | 8.6 | 0.49 | 3.4 | 1.02 |
| 1 | 2002 | 6.4 | 0.49 | 8.2 | 0.98 | 5.0 | 1.10 | 5.2 | 1.17 | 7.4 | 1.36 | 2.2 | 1.17 |
| 2 | 2003 | 5.4 | 1.50 | 7.2 | 1.17 | 3.4 | 1.02 | 4.6 | 1.85 | 6.2 | 2.04 | 1.4 | 0.49 |
| 3 | 2004 | 5.0 | 1.10 | 6.4 | 1.02 | 3.2 | 1.17 | 4.8 | 1.60 | 6.0 | 2.28 | 2.8 | 0.98 |
| 4 | 2005 | 3.8 | 1.17 | 5.0 | 1.10 | 1.6 | 0.80 | 5.2 | 0.75 | 8.4 | 0.80 | 2.0 | 1.10 |
Out[15]:
| song_id | valence_mean | valence_std | valence_ max_mean | valence_max_std | valence_min_mean | valence_min_std | arousal_mean | arousal_std | arousal_max_mean | arousal_max_std | arousal_min_mean | arousal_min_std | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 |
| mean | 2029.500000 | 4.923793 | 0.913448 | 5.918966 | 0.992586 | 3.864138 | 0.935000 | 4.856897 | 1.340690 | 6.116724 | 1.414310 | 3.305172 | 1.283103 |
| std | 16.886879 | 1.193966 | 0.388748 | 1.096843 | 0.362838 | 1.266040 | 0.330726 | 1.051779 | 0.449697 | 1.266050 | 0.531688 | 0.945087 | 0.358815 |
| min | 2001.000000 | 3.000000 | 0.370000 | 4.200000 | 0.400000 | 1.600000 | 0.370000 | 3.000000 | 0.370000 | 4.000000 | 0.470000 | 1.400000 | 0.490000 |
| 25% | 2015.250000 | 3.807500 | 0.492500 | 5.000000 | 0.750000 | 2.800000 | 0.750000 | 4.250000 | 1.117500 | 5.200000 | 1.020000 | 2.600000 | 1.020000 |
| 50% | 2029.500000 | 5.000000 | 0.925000 | 6.000000 | 0.980000 | 3.900000 | 0.920000 | 4.635000 | 1.360000 | 6.100000 | 1.465000 | 3.085000 | 1.260000 |
| 75% | 2043.750000 | 5.950000 | 1.170000 | 6.822500 | 1.200000 | 4.900000 | 1.170000 | 5.400000 | 1.615000 | 7.197500 | 1.790000 | 3.950000 | 1.522500 |
| max | 2058.000000 | 7.200000 | 1.850000 | 8.200000 | 1.790000 | 6.400000 | 1.670000 | 7.000000 | 2.330000 | 8.600000 | 2.580000 | 5.800000 | 2.150000 |
The remaining data are mp3 files and csv's with one-rater-per-song data (from which the above csv's were compiled).¶
In [16]:
valence_df = pd.read_csv(valence_path, index_col=0)
arousal_df = pd.read_csv(arousal_path, index_col=0)
static_df = pd.read_csv(static_1_path)
static_df.columns = static_df.columns.str.strip()
In [17]:
print("Original valence range:", static_df['valence_mean'].min(), "to", static_df['valence_mean'].max())
print("Original arousal range:", static_df['arousal_mean'].min(), "to", static_df['arousal_mean'].max())
Original valence range: 1.6 to 8.4 Original arousal range: 1.6 to 8.1
In [18]:
# Min-max normalize valence and arousal
static_df['valence_mean'] = static_df['valence_mean'].clip(0, 9) / 9.0
static_df['arousal_mean'] = static_df['arousal_mean'].clip(0, 9) / 9.0
In [19]:
print("Normalized valence range:", static_df['valence_mean'].min(), "to", static_df['valence_mean'].max())
print("Normalized arousal range:", static_df['arousal_mean'].min(), "to", static_df['arousal_mean'].max())
Normalized valence range: 0.17777777777777778 to 0.9333333333333333 Normalized arousal range: 0.17777777777777778 to 0.8999999999999999
In [20]:
def preprocess_static_songs(mp3_files,
static_df,
sr=22050,
n_mels=128,
hop_length=256,
duration_sec=30,
top_db=80):
"""
Returns
-------
X_real : (N, 128, 2560, 1) 30-second mel-spectrograms
conditions : (N, 2) valence_mean, arousal_mean per song
"""
X_real, conditions = [], []
target_frames = int(np.ceil((sr * duration_sec) / hop_length)) # 2560 frames for 30s
for mp3 in tqdm(mp3_files):
song_id = int(os.path.splitext(os.path.basename(mp3))[0])
if song_id not in static_df.index:
continue
try:
valence = static_df.loc[song_id, 'valence_mean']
arousal = static_df.loc[song_id, 'arousal_mean']
except KeyError:
continue
# Load *only* the first 30 seconds
try:
y, _ = librosa.load(mp3, sr=sr, duration=duration_sec)
except Exception as e:
print(f"Failed to load {mp3}: {e}")
continue
mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels, hop_length=hop_length)
mel_db = librosa.power_to_db(mel, ref=np.max, top_db=top_db)
mel_norm = np.clip((mel_db + top_db) / top_db, 0, 1)
# Pad or crop to exactly 2560 frames (30s)
if mel_norm.shape[1] < target_frames:
pad = target_frames - mel_norm.shape[1]
mel_norm = np.pad(mel_norm, ((0, 0), (0, pad)), mode='constant')
else:
mel_norm = mel_norm[:, :target_frames]
X_real.append(mel_norm[..., np.newaxis]) # shape: (128, 2560, 1)
conditions.append([valence, arousal])
X_real = np.array(X_real, dtype=np.float32)
X_real = (X_real * 2) - 1 # scale to [-1, 1]
conditions = np.array(conditions, dtype=np.float32)
return X_real, conditions
In [21]:
static_df.index = static_df.index.astype(int)
valid_ids = set(static_df.index)
valid_mp3_files = [f for f in mp3_files
if int(os.path.splitext(os.path.basename(f))[0]) in valid_ids]
ordered_ids = sorted(valid_ids) # Ensure ascending order
ordered_mp3_files = []
for sid in ordered_ids:
path = os.path.join(audio_dir, f"{sid}.mp3")
if os.path.exists(path):
ordered_mp3_files.append(path)
print(f"Found {len(ordered_mp3_files)} valid files")
from tqdm import tqdm
import numpy as np
X_real, conditions = preprocess_static_songs(ordered_mp3_files, static_df)
Found 1487 valid files
100%|██████████| 1487/1487 [03:50<00:00, 6.45it/s]
In [22]:
# Trim spectrograms to (128, 2560, 1)
X_real = X_real[:, :, :2560, :]
In [23]:
print(X_real.shape)
print(conditions.shape)
(1487, 128, 2560, 1) (1487, 2)
Verify that our "conditions" data match the original CSV valence and arousal means¶
In [24]:
import random
import os
example_index = random.randint(0, len(ordered_mp3_files) - 1)
example_file = ordered_mp3_files[example_index]
filename = os.path.splitext(os.path.basename(example_file))[0]
song_id = int(filename)
print(f"Checking song_id: {song_id}")
v_csv = static_df.loc[song_id, 'valence_mean']
a_csv = static_df.loc[song_id, 'arousal_mean']
v_extracted = conditions[example_index, 0]
a_extracted = conditions[example_index, 1]
print(f"Valence → CSV: {v_csv:.4f} | Extracted: {v_extracted:.4f}")
print(f"Arousal → CSV: {a_csv:.4f} | Extracted: {a_extracted:.4f}")
Checking song_id: 775 Valence → CSV: 0.3667 | Extracted: 0.3667 Arousal → CSV: 0.4556 | Extracted: 0.4556
CVAE Training Below (Conditional VAE) with annealing to support
In [26]:
import tensorflow as tf
from tensorflow.keras import layers, Model, Input
from tensorflow.keras.losses import MeanSquaredError
from tensorflow.keras.optimizers import Adam
latent_dim = 256
mel_shape = (128, 2560, 1)
cond_dim = 2
def build_encoder():
mel_input = Input(shape=mel_shape)
cond_input = Input(shape=(cond_dim,))
x = layers.Conv2D(32, 4, strides=2, padding="same", activation="relu")(mel_input)
x = layers.Conv2D(64, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2D(128, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2D(256, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2D(512, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Flatten()(x)
x = layers.Concatenate()([x, cond_input])
x = layers.Dense(512, activation="relu")(x)
z_mean = layers.Dense(latent_dim)(x)
z_log_var = layers.Dense(latent_dim)(x)
def sampling(args):
z_mean, z_log_var = args
epsilon = tf.random.normal(shape=tf.shape(z_mean))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
z = layers.Lambda(sampling)([z_mean, z_log_var])
return Model([mel_input, cond_input], [z_mean, z_log_var, z], name="encoder")
def build_decoder():
z_input = Input(shape=(latent_dim,))
cond_input = Input(shape=(cond_dim,))
x = layers.Concatenate()([z_input, cond_input])
x = layers.Dense(4 * 80 * 512, activation="relu")(x)
x = layers.Reshape((4, 80, 512))(x)
x = layers.Conv2DTranspose(256, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2DTranspose(128, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2DTranspose(64, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2DTranspose(32, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2DTranspose(16, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2DTranspose(1, 3, padding="same", activation="tanh")(x)
return Model([z_input, cond_input], x, name="decoder")
class CVAE(tf.keras.Model):
def __init__(self, encoder, decoder, kl_annealing_epochs=20, **kwargs):
super(CVAE, self).__init__(**kwargs)
self.encoder = encoder
self.decoder = decoder
self.kl_annealing_epochs = kl_annealing_epochs
self.total_loss_tracker = tf.keras.metrics.Mean(name="total_loss")
self.recon_loss_tracker = tf.keras.metrics.Mean(name="recon_loss")
self.kl_loss_tracker = tf.keras.metrics.Mean(name="kl_loss")
self.recon_loss_fn = MeanSquaredError()
def compile(self, optimizer):
super().compile()
self.optimizer = optimizer
def train_step(self, data):
(x_inputs, y_true) = data
x, c = x_inputs
step = tf.cast(self.optimizer.iterations, tf.float32)
kl_weight = tf.minimum(1.0, step / (self.kl_annealing_epochs * 1000.0))
with tf.GradientTape() as tape:
z_mean, z_log_var, z = self.encoder([x, c])
x_recon = self.decoder([z, c])
recon_loss = self.recon_loss_fn(y_true, x_recon)
kl_loss = -0.5 * tf.reduce_mean(1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))
total_loss = recon_loss + kl_weight * kl_loss
grads = tape.gradient(total_loss, self.trainable_weights)
self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
self.total_loss_tracker.update_state(total_loss)
self.recon_loss_tracker.update_state(recon_loss)
self.kl_loss_tracker.update_state(kl_loss)
return {
"loss": self.total_loss_tracker.result(),
"recon_loss": self.recon_loss_tracker.result(),
"kl_loss": self.kl_loss_tracker.result(),
"kl_weight": kl_weight,
}
In [27]:
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
encoder = build_encoder()
decoder = build_decoder()
cvae = CVAE(encoder, decoder, kl_annealing_epochs=20)
cvae.compile(optimizer=Adam(learning_rate=1e-4))
early_stop_cb = tf.keras.callbacks.EarlyStopping(
monitor="loss", patience=15, restore_best_weights=True
)
cvae.fit(
x=(X_real, conditions),
y=X_real,
epochs=100,
batch_size=32,
callbacks=[early_stop_cb]
)
Epoch 1/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 44s 361ms/step - kl_loss: 0.2596 - kl_weight: 0.0012 - loss: 0.1859 - recon_loss: 0.1855 Epoch 2/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.6937 - kl_weight: 0.0035 - loss: 0.0971 - recon_loss: 0.0951 Epoch 3/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.3242 - kl_weight: 0.0059 - loss: 0.0871 - recon_loss: 0.0855 Epoch 4/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.2248 - kl_weight: 0.0082 - loss: 0.0821 - recon_loss: 0.0804 Epoch 5/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.2053 - kl_weight: 0.0106 - loss: 0.0803 - recon_loss: 0.0782 Epoch 6/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.1689 - kl_weight: 0.0129 - loss: 0.0787 - recon_loss: 0.0766 Epoch 7/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1583 - kl_weight: 0.0153 - loss: 0.0753 - recon_loss: 0.0730 Epoch 8/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1566 - kl_weight: 0.0176 - loss: 0.0720 - recon_loss: 0.0693 Epoch 9/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1508 - kl_weight: 0.0200 - loss: 0.0643 - recon_loss: 0.0614 Epoch 10/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1514 - kl_weight: 0.0223 - loss: 0.0626 - recon_loss: 0.0593 Epoch 11/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1419 - kl_weight: 0.0247 - loss: 0.0601 - recon_loss: 0.0567 Epoch 12/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1390 - kl_weight: 0.0270 - loss: 0.0605 - recon_loss: 0.0569 Epoch 13/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 80ms/step - kl_loss: 0.1350 - kl_weight: 0.0294 - loss: 0.0594 - recon_loss: 0.0555 Epoch 14/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1271 - kl_weight: 0.0317 - loss: 0.0607 - recon_loss: 0.0567 Epoch 15/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1215 - kl_weight: 0.0341 - loss: 0.0588 - recon_loss: 0.0547 Epoch 16/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1228 - kl_weight: 0.0364 - loss: 0.0592 - recon_loss: 0.0548 Epoch 17/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1148 - kl_weight: 0.0388 - loss: 0.0585 - recon_loss: 0.0541 Epoch 18/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.1088 - kl_weight: 0.0411 - loss: 0.0586 - recon_loss: 0.0542 Epoch 19/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1057 - kl_weight: 0.0435 - loss: 0.0572 - recon_loss: 0.0526 Epoch 20/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0992 - kl_weight: 0.0458 - loss: 0.0578 - recon_loss: 0.0534 Epoch 21/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0952 - kl_weight: 0.0482 - loss: 0.0576 - recon_loss: 0.0531 Epoch 22/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.0975 - kl_weight: 0.0505 - loss: 0.0582 - recon_loss: 0.0533 Epoch 23/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0920 - kl_weight: 0.0529 - loss: 0.0582 - recon_loss: 0.0534 Epoch 24/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0931 - kl_weight: 0.0552 - loss: 0.0587 - recon_loss: 0.0536 Epoch 25/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0878 - kl_weight: 0.0576 - loss: 0.0584 - recon_loss: 0.0534 Epoch 26/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0811 - kl_weight: 0.0599 - loss: 0.0579 - recon_loss: 0.0531 Epoch 27/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0802 - kl_weight: 0.0623 - loss: 0.0581 - recon_loss: 0.0531 Epoch 28/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0802 - kl_weight: 0.0646 - loss: 0.0581 - recon_loss: 0.0530 Epoch 29/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0783 - kl_weight: 0.0670 - loss: 0.0587 - recon_loss: 0.0535 Epoch 30/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0736 - kl_weight: 0.0693 - loss: 0.0579 - recon_loss: 0.0528 Epoch 31/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0754 - kl_weight: 0.0717 - loss: 0.0573 - recon_loss: 0.0520 Epoch 32/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0721 - kl_weight: 0.0740 - loss: 0.0575 - recon_loss: 0.0522 Epoch 33/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0705 - kl_weight: 0.0764 - loss: 0.0569 - recon_loss: 0.0516 Epoch 34/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0684 - kl_weight: 0.0787 - loss: 0.0575 - recon_loss: 0.0522 Epoch 35/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0667 - kl_weight: 0.0811 - loss: 0.0568 - recon_loss: 0.0514 Epoch 36/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.0658 - kl_weight: 0.0834 - loss: 0.0567 - recon_loss: 0.0513 Epoch 37/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0645 - kl_weight: 0.0858 - loss: 0.0575 - recon_loss: 0.0520 Epoch 38/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0637 - kl_weight: 0.0881 - loss: 0.0572 - recon_loss: 0.0516 Epoch 39/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0621 - kl_weight: 0.0905 - loss: 0.0579 - recon_loss: 0.0523 Epoch 40/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.0610 - kl_weight: 0.0928 - loss: 0.0568 - recon_loss: 0.0512 Epoch 41/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0579 - kl_weight: 0.0952 - loss: 0.0564 - recon_loss: 0.0509 Epoch 42/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0569 - kl_weight: 0.0975 - loss: 0.0570 - recon_loss: 0.0515 Epoch 43/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 80ms/step - kl_loss: 0.0563 - kl_weight: 0.0999 - loss: 0.0564 - recon_loss: 0.0508 Epoch 44/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0566 - kl_weight: 0.1022 - loss: 0.0569 - recon_loss: 0.0511 Epoch 45/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 73ms/step - kl_loss: 0.0544 - kl_weight: 0.1046 - loss: 0.0563 - recon_loss: 0.0506 Epoch 46/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0516 - kl_weight: 0.1069 - loss: 0.0572 - recon_loss: 0.0517 Epoch 47/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0519 - kl_weight: 0.1093 - loss: 0.0576 - recon_loss: 0.0520 Epoch 48/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0504 - kl_weight: 0.1116 - loss: 0.0571 - recon_loss: 0.0515 Epoch 49/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0499 - kl_weight: 0.1140 - loss: 0.0573 - recon_loss: 0.0516 Epoch 50/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0488 - kl_weight: 0.1163 - loss: 0.0569 - recon_loss: 0.0513 Epoch 51/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0496 - kl_weight: 0.1187 - loss: 0.0566 - recon_loss: 0.0507 Epoch 52/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0474 - kl_weight: 0.1210 - loss: 0.0567 - recon_loss: 0.0509 Epoch 53/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0480 - kl_weight: 0.1234 - loss: 0.0576 - recon_loss: 0.0517 Epoch 54/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0450 - kl_weight: 0.1257 - loss: 0.0577 - recon_loss: 0.0521 Epoch 55/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0452 - kl_weight: 0.1281 - loss: 0.0574 - recon_loss: 0.0516 Epoch 56/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0448 - kl_weight: 0.1304 - loss: 0.0580 - recon_loss: 0.0521 Epoch 57/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0449 - kl_weight: 0.1328 - loss: 0.0571 - recon_loss: 0.0512 Epoch 58/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0407 - kl_weight: 0.1351 - loss: 0.0565 - recon_loss: 0.0510
Out[27]:
<keras.src.callbacks.history.History at 0x7910cb6e1f50>
In [28]:
import numpy as np
import librosa
import librosa.display
import matplotlib.pyplot as plt
condition = np.array([[0.9, 0.8]], dtype=np.float32)
z = tf.random.normal((1, latent_dim))
generated = decoder.predict([z, condition])
mel = generated[0, :, :, 0]
mel_db = ((mel + 1) / 2) * 80 - 80
plt.figure(figsize=(12, 4))
librosa.display.specshow(mel_db, sr=22050, hop_length=256, x_axis='time', y_axis='mel')
plt.colorbar(format="%+2.0f dB")
plt.title("Generated 30-Second Mel Spectrogram")
plt.tight_layout()
plt.show()
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 832ms/step
Pick a similarly labeled spectrogram from the data to compare next to our generated spectrogram
In [29]:
import numpy as np
import librosa.display
import matplotlib.pyplot as plt
target_condition = np.array([0.9, 0.8], dtype=np.float32)
distances = np.linalg.norm(conditions - target_condition, axis=1)
closest_index = np.argmin(distances)
print(f"Closest match index: {closest_index}, condition: {conditions[closest_index]}")
mel_real = X_real[closest_index, :, :, 0]
mel_real_db = ((mel_real + 1) / 2) * 80 - 80
mel_generated_db = ((mel + 1) / 2) * 80 - 80
fig, axs = plt.subplots(1, 2, figsize=(14, 4))
img1 = librosa.display.specshow(
mel_real_db, sr=22050, hop_length=256,
y_axis='mel', x_axis='time', ax=axs[0]
)
axs[0].set_title(f"Closest Real Spectrogram\nValence: {conditions[closest_index][0]:.2f}, Arousal: {conditions[closest_index][1]:.2f}")
fig.colorbar(img1, ax=axs[0], format="%+2.0f dB")
img2 = librosa.display.specshow(
mel_generated_db, sr=22050, hop_length=256,
y_axis='mel', x_axis='time', ax=axs[1]
)
axs[1].set_title("Generated Spectrogram\nCondition: [0.9, 0.8]")
fig.colorbar(img2, ax=axs[1], format="%+2.0f dB")
plt.tight_layout()
plt.show()
Closest match index: 1247, condition: [0.87777776 0.7888889 ]
In [44]:
from google.colab import files
np.save("generated_mel.npy", mel)
print("Saved as generated_mel.npy")
files.download("generated_mel.npy")
Saved as generated_mel.npy
Below is GAN training to compare performance
In [37]:
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, Model, Input
from tensorflow.keras.optimizers import Adam
latent_dim = 256
mel_shape = (128, 2560, 1)
cond_dim = 2
In [38]:
def build_generator():
z_input = Input(shape=(latent_dim,))
c_input = Input(shape=(cond_dim,))
x = layers.Concatenate()([z_input, c_input])
x = layers.Dense(8 * 160 * 1024, activation="relu")(x)
x = layers.Reshape((8, 160, 1024))(x)
x = layers.Conv2DTranspose(512, 4, strides=2, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.3)(x)
x = layers.ReLU()(x)
x = layers.Conv2DTranspose(256, 4, strides=2, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.3)(x)
x = layers.ReLU()(x)
x = layers.Conv2DTranspose(128, 4, strides=2, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.3)(x)
x = layers.ReLU()(x)
x = layers.Conv2DTranspose(64, 4, strides=2, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.3)(x)
x = layers.ReLU()(x)
x = layers.Conv2DTranspose(1, 3, padding='same', activation='tanh')(x)
return Model([z_input, c_input], x, name="generator")
In [39]:
def build_discriminator():
mel_input = Input(shape=mel_shape)
c_input = Input(shape=(cond_dim,))
x = layers.Conv2D(64, 4, strides=2, padding='same')(mel_input)
x = layers.LeakyReLU(negative_slope=0.2)(x)
x = layers.Dropout(0.3)(x)
x = layers.Conv2D(128, 4, strides=2, padding='same')(x)
x = layers.LeakyReLU(negative_slope=0.2)(x)
x = layers.Dropout(0.3)(x)
# x = layers.Conv2D(256, 4, strides=2, padding='same')(x)
# x = layers.LeakyReLU(negative_slope=0.2)(x)
# x = layers.Dropout(0.3)(x)
x = layers.Flatten()(x)
x = layers.Concatenate()([x, c_input])
x = layers.Dense(1)(x)
return Model([mel_input, c_input], x, name="discriminator")
In [40]:
generator = build_generator()
discriminator = build_discriminator()
discriminator.compile(optimizer=Adam(1e-4), loss=tf.keras.losses.BinaryCrossentropy(from_logits=True))
z_input = Input(shape=(latent_dim,))
c_input = Input(shape=(cond_dim,))
fake_mel = generator([z_input, c_input])
validity = discriminator([fake_mel, c_input])
gan = Model([z_input, c_input], validity)
gan.compile(optimizer=Adam(1e-5), loss=tf.keras.losses.BinaryCrossentropy(from_logits=True))
In [41]:
def train_gan(X_real, C_real, epochs=50, batch_size=8):
steps_per_epoch = X_real.shape[0] // batch_size
for epoch in range(epochs):
d_losses, g_losses = [], []
for _ in range(steps_per_epoch):
idx = np.random.randint(0, X_real.shape[0], batch_size)
real_imgs = X_real[idx]
real_cond = C_real[idx]
z_noise = np.random.normal(0, 1, (batch_size, latent_dim))
fake_imgs = generator([z_noise, real_cond], training=False)
#real_labels = np.ones((batch_size, 1)) * 0.9
#fake_labels = np.zeros((batch_size, 1))
real_labels = np.ones((batch_size, 1)) * np.random.uniform(0.7, 1.0)
fake_labels = np.zeros((batch_size, 1)) + np.random.uniform(0.0, 0.3)
real_imgs += np.random.normal(0, 0.05, size=real_imgs.shape)
fake_imgs += np.random.normal(0, 0.05, size=fake_imgs.shape)
discriminator.trainable = True
d_loss_real = discriminator.train_on_batch([real_imgs, real_cond], real_labels)
d_loss_fake = discriminator.train_on_batch([fake_imgs, real_cond], fake_labels)
d_loss = 0.5 * (d_loss_real + d_loss_fake)
d_losses.append(d_loss)
discriminator.trainable = False
# Train generator more than once
for _ in range(4):
z_noise = np.random.normal(0, 1, (batch_size, latent_dim))
g_loss = gan.train_on_batch([z_noise, real_cond], np.ones((batch_size, 1)))
g_losses.append(g_loss)
# z_noise = np.random.normal(0, 1, (batch_size, latent_dim))
# g_loss = gan.train_on_batch([z_noise, real_cond], np.ones((batch_size, 1)))
# g_losses.append(g_loss)
print(f"Epoch {epoch+1}/{epochs} - D: {np.mean(d_losses):.4f}, G: {np.mean(g_losses):.4f}")
This GAN was very hard to train. Getting the discriminator and generator to train without one dominating the other was extremely challenging. Solutions to this issue included the addition of dropout layers, noisy fake and real labels, and allowing the generator to train four times the amount of the discriminator
In [42]:
train_gan(
X_real=X_real,
C_real=conditions,
epochs=50,
batch_size=8
)
Epoch 1/50 - D: 0.5562, G: 1.5320 Epoch 2/50 - D: 0.6227, G: 2.7005 Epoch 3/50 - D: 0.5935, G: 2.5103 Epoch 4/50 - D: 0.6127, G: 2.2668 Epoch 5/50 - D: 0.6127, G: 2.0006 Epoch 6/50 - D: 0.6096, G: 1.8111 Epoch 7/50 - D: 0.6081, G: 1.6729 Epoch 8/50 - D: 0.6042, G: 1.5422 Epoch 9/50 - D: 0.5990, G: 1.4564 Epoch 10/50 - D: 0.5915, G: 1.3923 Epoch 11/50 - D: 0.5890, G: 1.3206 Epoch 12/50 - D: 0.5879, G: 1.2621 Epoch 13/50 - D: 0.5862, G: 1.2097 Epoch 14/50 - D: 0.5852, G: 1.1667 Epoch 15/50 - D: 0.5831, G: 1.1344 Epoch 16/50 - D: 0.5832, G: 1.1047 Epoch 17/50 - D: 0.5821, G: 1.0721 Epoch 18/50 - D: 0.5797, G: 1.0482 Epoch 19/50 - D: 0.5790, G: 1.0301 Epoch 20/50 - D: 0.5789, G: 1.0101 Epoch 21/50 - D: 0.5790, G: 0.9877 Epoch 22/50 - D: 0.5788, G: 0.9647 Epoch 23/50 - D: 0.5783, G: 0.9412 Epoch 24/50 - D: 0.5788, G: 0.9247 Epoch 25/50 - D: 0.5770, G: 0.9047 Epoch 26/50 - D: 0.5754, G: 0.8869 Epoch 27/50 - D: 0.5763, G: 0.8729 Epoch 28/50 - D: 0.5767, G: 0.8608 Epoch 29/50 - D: 0.5775, G: 0.8467 Epoch 30/50 - D: 0.5778, G: 0.8382 Epoch 31/50 - D: 0.5785, G: 0.8301 Epoch 32/50 - D: 0.5788, G: 0.8202 Epoch 33/50 - D: 0.5788, G: 0.8065 Epoch 34/50 - D: 0.5806, G: 0.8003 Epoch 35/50 - D: 0.5811, G: 0.7938 Epoch 36/50 - D: 0.5815, G: 0.7887 Epoch 37/50 - D: 0.5810, G: 0.7810 Epoch 38/50 - D: 0.5816, G: 0.7751 Epoch 39/50 - D: 0.5821, G: 0.7699 Epoch 40/50 - D: 0.5824, G: 0.7660 Epoch 41/50 - D: 0.5824, G: 0.7659 Epoch 42/50 - D: 0.5834, G: 0.7612 Epoch 43/50 - D: 0.5848, G: 0.7556 Epoch 44/50 - D: 0.5864, G: 0.7512 Epoch 45/50 - D: 0.5862, G: 0.7441 Epoch 46/50 - D: 0.5874, G: 0.7395 Epoch 47/50 - D: 0.5883, G: 0.7347 Epoch 48/50 - D: 0.5886, G: 0.7282 Epoch 49/50 - D: 0.5891, G: 0.7220 Epoch 50/50 - D: 0.5896, G: 0.7182
In [50]:
import numpy as np
import librosa
import librosa.display
import matplotlib.pyplot as plt
import tensorflow as tf
def compare_gan_generated_vs_real(generator, latent_dim, condition_vec, X_real, conditions, sr=22050, hop_length=256):
condition = np.array([condition_vec], dtype=np.float32)
z = tf.random.normal((1, latent_dim))
generated = generator.predict([z, condition], verbose=0)
mel_generated = generated[0, :, :, 0]
mel_generated_db = ((mel_generated + 1) / 2) * 80 - 80
distances = np.linalg.norm(conditions - condition_vec, axis=1)
closest_index = np.argmin(distances)
mel_real = X_real[closest_index, :, :, 0]
mel_real_db = ((mel_real + 1) / 2) * 80 - 80
print(f"Closest real match: valence={conditions[closest_index][0]:.2f}, arousal={conditions[closest_index][1]:.2f}")
fig, axs = plt.subplots(1, 2, figsize=(14, 4))
img1 = librosa.display.specshow(mel_real_db, sr=sr, hop_length=hop_length, y_axis='mel', x_axis='time', ax=axs[0])
axs[0].set_title(f"Closest Real\nValence: {conditions[closest_index][0]:.2f}, Arousal: {conditions[closest_index][1]:.2f}")
fig.colorbar(img1, ax=axs[0], format="%+2.0f dB")
img2 = librosa.display.specshow(mel_generated_db, sr=sr, hop_length=hop_length, y_axis='mel', x_axis='time', ax=axs[1])
axs[1].set_title(f"GAN Generated\nCondition: {condition_vec}")
fig.colorbar(img2, ax=axs[1], format="%+2.0f dB")
plt.tight_layout()
plt.show()
return mel_generated
In [51]:
compare_gan_generated_vs_real(
generator=generator,
latent_dim=latent_dim,
condition_vec=[0.9, 0.8],
X_real=X_real,
conditions=conditions
)
Closest real match: valence=0.88, arousal=0.79
Out[51]:
array([[-0.387577 , -0.3413093 , -0.41056788, ..., -0.1436869 ,
-0.13405532, -0.01537604],
[-0.3744087 , -0.27375042, -0.02399779, ..., 0.15888882,
0.22593392, 0.30883482],
[-0.58776706, -0.36681405, -0.35601607, ..., 0.19211851,
0.21851835, 0.2894638 ],
...,
[-0.9171173 , -0.9641639 , -0.979369 , ..., -0.8538212 ,
-0.81623214, -0.695855 ],
[-0.9315934 , -0.98273087, -0.99111694, ..., -0.8547327 ,
-0.84214276, -0.73094314],
[-0.9226168 , -0.98312634, -0.9919821 , ..., -0.9410282 ,
-0.91658026, -0.757629 ]], dtype=float32)
In [55]:
mel = compare_gan_generated_vs_real(generator, latent_dim, [0.9, 0.8], X_real, conditions)
np.save("gan_generated_mel.npy", mel)
files.download("gan_generated_mel.npy")
Closest real match: valence=0.88, arousal=0.79
